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Related Experiment Video

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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

A robust brain deformation framework based on a finite element model in IGNS.

Yixun Liu¹, Zhijian Song

¹Digital Medical Research Center of Fudan University, Shanghai 200032, People's Republic of China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|April 3, 2008

Summary

Non-rigid registration effectively corrects brain deformation in image-guided neurosurgery. This method uses a laser range scanner to track surface changes, improving surgical accuracy.

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Area of Science:

Neurosurgery
Medical Imaging
Biomedical Engineering

Background:

Brain deformation causes inaccuracies in image-guided neurosurgery.
Existing solutions include intra-operative imaging, deformation atlases, and non-rigid registration.
Non-rigid registration, particularly using linear elastic models, shows promise for clinical application.

Purpose of the Study:

To develop and validate a framework for correcting brain deformation during neurosurgery.
To assess the feasibility of non-rigid registration for real-time deformation correction.

Main Methods:

A framework based on non-rigid registration was designed to correct brain deformation.
A laser range scanner (LRS) was integrated to capture intra-operative brain surface data.
A novel surface-tracking algorithm involving rigid registration and surface movement simulation (thin-plate spline) was developed.

Main Results:

The framework was tested on pigs, accurately tracking brain surface deformation.
Internal brain deformation was estimated by comparing pre-operative MRI and intra-operative LRS data.
Pre-operative images were successfully corrected based on the estimated deformation.

Conclusions:

The developed framework effectively captures brain surface deformation.
The method accurately estimates internal brain deformation.
This approach holds potential for improving accuracy in image-guided neurosurgery.